A Learning Analytics Approach towards Understanding Collaborative Inquiry in a Problem-Based Learning Environment

Autor/inn/en	Saleh, Asmalina; Phillips, Tanner M.; Hmelo-Silver, Cindy E.; Glazewski, Krista D.; Mott, Bradford W.; Lester, James C.
Titel	A Learning Analytics Approach towards Understanding Collaborative Inquiry in a Problem-Based Learning Environment
Quelle	In: British Journal of Educational Technology, 53 (2022) 5, S.1321-1342 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Saleh, Asmalina)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13198
Schlagwörter	Cooperative Learning; Learning Analytics; Pretests Posttests; Problem Based Learning; Teaching Methods; Game Based Learning; Factor Analysis; Brainstorming; Science Instruction; Inquiry; Active Learning; Achievement Gains; Independent Study; Learning Processes; Course Content; Interaction Process Analysis + Suchen Sie Ihr Suchwort? Kooperatives Lernen; Problem-based learning; Problemorientiertes Lernen; Teaching method; Lehrmethode; Unterrichtsmethode; Faktorenanalyse; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Aktives Lernen; Achievement gain; Leistungssteigerung; Selbststudium; Learning process; Lernprozess; Kursprogramm; Prozessanalyse
Abstract	This exploratory paper highlights how problem-based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from "Crystal Island: EcoJourneys," a collaborative game-based learning environment centred on supporting science inquiry. In "Crystal Island: EcoJourneys," students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in-game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game-based learning environment? Drawing on a mixed method approach, we first analyzed students' pre- and post-test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre- and post-test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game-based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game-based learning environment: students engaged in low to moderate self-directed actions with: (1) high and (2) moderate collaborative sense-making actions; (3) low self-directed with low collaborative sense-making actions; and (4) high self-directed actions with low collaborative sense-making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self-directed and high collaborative sense-making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in-game activities. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01